Teleology, Shape Bias and Transformations
David Rose, Jadess Lowery, Siying Zhang and Ellen Markman
September 10, 2024
- 1 Load packages
- 2 Settings
- 3 EXPERIMENT 1 (Basic-level)
- 4 DATA
- 5 STATS
- 6 EXPERIMENT 2 (Superordinate-level)
- 7 DATA
- 8 STATS
- 9 PART 2 (What one X or Y, is for this?)
- 10 EXPERIMENT 3 (Transformation)
- 11 STATS
- 12 PLOTS
- 13 SESSION INFO
1 Load packages
2 Settings
3 EXPERIMENT 1 (Basic-level)
4 DATA
4.2 Wrangle
df.exp1 = df.exp1 %>%
filter(include == 1) %>%
pivot_longer(cols = (bag: snail),
names_to = "item",
values_to = "rating",
values_drop_na = TRUE) %>%
relocate(participant_number, condition, item, rating, age, gender, lan_spoken) %>%
mutate(kind = case_when(item == "bag" ~ 0,
item == "shoe" ~ 0,
item == "clock" ~ 0,
item == "phone" ~ 0,
item == "drum" ~ 0,
item == "spider" ~ 1,
item == "tree" ~ 1,
item == "snail" ~ 1)) %>%
mutate(kind = factor(kind,
levels = c("0", "1"),
labels = c("artifact", "biological")))
# warm-up items
df.exp1.warmup = df.exp1 %>%
filter(item %in% c("bag", "shoe"))
#test items
df.exp1 = df.exp1 %>%
filter(!item %in% c("bag", "shoe"))4.3 Demographics
## # A tibble: 3 × 2
## gender n
## <chr> <int>
## 1 "female" 86
## 2 "male" 92
## 3 "male " 1 # language spoken
df.exp1 %>%
mutate(en_occurrence = ifelse(str_detect(lan_spoken, "en"), "en", "non-en")) %>%
group_by(en_occurrence) %>%
summarise(n = n_distinct(participant_number)) %>%
print()## # A tibble: 2 × 2
## en_occurrence n
## <chr> <int>
## 1 en 172
## 2 non-en 65 STATS
5.1 Bayesian Regression
# sum coding
options(contrasts = c("contr.sum","contr.poly"))
df.model = df.exp1 %>%
mutate(condition = as_factor(condition),
condition= fct_relevel(condition, "purpose"))
# Check contrast values
contrasts(df.model$condition)## [,1] [,2]
## purpose 1 0
## label_only 0 1
## irrelevant -1 -1fit.brm.basic_level = brm(formula = rating ~ 1 + condition + (1 | participant_number) + (1 | item),
data = df.model,
family = "bernoulli",
seed = 1,
iter = 8000,
warmup = 1000,
cores = 8,
control = list(adapt_delta = .999999,
max_treedepth = 15),
file = "cache/fit.brm.basic_level")
fit.brm.basic_level## Family: bernoulli
## Links: mu = logit
## Formula: rating ~ 1 + condition + (1 | participant_number) + (1 | item)
## Data: df.model (Number of observations: 1080)
## Draws: 4 chains, each with iter = 8000; warmup = 1000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.25 0.18 0.02 0.70 1.00 6924 8535
##
## ~participant_number (Number of levels: 178)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.70 0.12 0.47 0.94 1.00 9286 12959
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.10 0.15 -0.20 0.40 1.00 15424 14750
## condition1 0.87 0.13 0.63 1.12 1.00 20955 20549
## condition2 -0.57 0.12 -0.81 -0.34 1.00 21520 20793
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).5.2 Hypotheis test of condition differneces and differnece from chance
## $emmeans
## condition emmean lower.HPD upper.HPD
## purpose 0.965 0.573 1.357
## label_only -0.474 -0.867 -0.113
## irrelevant -0.199 -0.575 0.186
##
## Point estimate displayed: median
## Results are given on the logit (not the response) scale.
## HPD interval probability: 0.95
##
## $contrasts
## contrast estimate lower.HPD upper.HPD
## purpose - label_only 1.437 1.018 1.862
## purpose - irrelevant 1.161 0.740 1.581
## label_only - irrelevant -0.274 -0.685 0.124
##
## Point estimate displayed: median
## Results are given on the log odds ratio (not the response) scale.
## HPD interval probability: 0.955.3 Test of kind
fit.brm.basic_level.kind = brm(formula = rating ~ 1 + kind + (1 | participant_number) + (1 | item),
data = df.model,
family = "bernoulli",
seed = 1,
iter = 8000,
warmup = 1000,
cores = 8,
control = list(adapt_delta = .999999,
max_treedepth = 15),
file = "cache/fit.brm.basic_leve.kindl")
fit.brm.basic_level.kind## Family: bernoulli
## Links: mu = logit
## Formula: rating ~ 1 + kind + (1 | participant_number) + (1 | item)
## Data: df.model (Number of observations: 1080)
## Draws: 4 chains, each with iter = 8000; warmup = 1000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.20 0.19 0.01 0.69 1.00 6788 9912
##
## ~participant_number (Number of levels: 178)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.95 0.12 0.73 1.19 1.00 10870 17067
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.09 0.15 -0.21 0.37 1.00 16089 15202
## kind1 0.16 0.13 -0.10 0.42 1.00 15735 10645
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#PLOT
5.4 Condition
df.exp1 = read.csv("../../data/exp1/exp1.csv")
df.plot = df.exp1 %>%
filter(include == 1) %>%
select(-bag, -shoe) %>%
mutate(across(.cols = clock : snail, .fns = as.numeric)) %>%
mutate(total = clock + drum + phone + tree + spider + snail,
percent = (total / 6) * 100) %>%
mutate(condition = str_replace_all(condition, "_", " ")) %>%
mutate(condition = factor(condition, levels = c("label only", "irrelevant", "purpose"))) %>%
select(participant_number, condition, percent)
p1 = ggplot(data = df.plot,
mapping = aes(x = condition,
y = percent,
group = condition,
color = condition,
fill = condition)) +
stat_summary(fun = mean,
geom = "bar",
position = position_dodge(width = 0.5),
alpha = .9,
color = "black",
size = 1) +
geom_hline(yintercept = 50, linetype="dashed",
color = "black", size=.5) +
geom_point(aes(color = condition,
fill = condition),
position = position_jitterdodge(dodge.width = 0.8,
jitter.width = 0.5,
jitter.height = 0.0),
shape = 21,
size = 2,
color = "grey30",
alpha = .4) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
position = position_dodge(width = 0.5),
color = "black",
size = 1.2) +
scale_fill_manual(values = c("label only" = "#377EB8", "irrelevant" = "#E41A1C", "purpose" = "#4DAF4A"), breaks=c("label only", "irrelevant", "purpose")) +
scale_color_manual(values = c("label only" = "#377EB8", "irrelevant" = "#E41A1C", "purpose" = "#4DAF4A"), breaks=c("label only", "irrelevant", "purpose")) +
scale_y_continuous(breaks = seq(0, 100, 25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 100)) +
labs(y = "Probability of selecting \n\ taxonomic match") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size=18),
axis.text.y = element_text(size = 14),
legend.title = element_blank(),
legend.text = element_text(size = 14),
legend.position = "bottom",
axis.text.x = element_blank(),
plot.title = element_text(hjust = 0.5, face="bold", size=26, margin = margin(t = 20, b = 20))) +
ggtitle("Overall")
p1
5.5 Kind by condition
df.exp1 = read.csv("../../data/exp1/exp1.csv")
df.plot = df.exp1 %>%
filter(include == 1) %>%
select(-bag, -shoe) %>%
pivot_longer(cols = (clock: snail),
names_to = "item",
values_to = "rating",
values_drop_na = TRUE) %>%
relocate(participant_number, condition, item, rating, age) %>%
select(participant_number: age) %>%
mutate(kind = case_when(item == "clock" ~ 0,
item == "phone" ~ 0,
item == "drum" ~ 0,
item == "spider" ~ 1,
item == "tree" ~ 1,
item == "snail" ~ 1)) %>%
mutate(kind = factor(kind,
levels = c("0", "1"),
labels = c("artifacts", "biological kinds"))) %>%
group_by(participant_number, condition, kind) %>%
summarise(total = sum(rating),
percent = (total / 3) * 100) %>%
mutate(condition = str_replace_all(condition, "_", " ")) %>%
mutate(condition = factor(condition, levels = c("label only", "irrelevant", "purpose"))) %>%
select(participant_number, condition, kind, total, percent)
p2 = ggplot(data = df.plot,
mapping = aes(x = condition,
y = percent,
group = kind,
color = kind,
fill = kind)) +
stat_summary(fun = mean,
geom = "bar",
position = position_dodge(width = 0.9),
alpha = .9,
color = "black",
size = 1) +
geom_hline(yintercept = 50, linetype="dashed",
color = "black", size=.5) +
geom_point(aes(color = kind,
fill = kind),
position = position_jitterdodge(dodge.width = 0.8,
jitter.width = 0.5,
jitter.height = 0.0),
shape = 21,
size = 2,
color = "grey30",
alpha = .4) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
position = position_dodge(width = 0.9),
color = "black",
size = 1.2) +
scale_fill_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
scale_color_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
scale_y_continuous(breaks = seq(0, 100, 25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 100)) +
labs(y = "Probability of selecting \n\ taxonomic match") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.title.y = element_text(size=18),
axis.text.y = element_text(size = 14),
legend.title = element_blank(),
legend.text = element_text(size = 14),
legend.position = "bottom",
plot.title = element_text(hjust = 0.5, face="bold", size=16, margin = margin(t = 20, b = 20))) +
ggtitle("Artifacts vs. Biological Kinds")
p2
5.6 Combine condition and kind plots
plot_basic = p1 + p2 &
plot_layout(nrow = 1) &
plot_annotation(title = "Basic-level categorization",
tag_levels = "A") &
theme(plot.tag = element_text(face = "bold", size = 24),
plot.title = element_text(face = "bold", hjust = 0.5, size = 24))
ggsave(plot_basic, filename = "../../figures/exp1/exp1_condition_kind_combined_bar.pdf", height = 5, width = 12)
plot_basic
6 EXPERIMENT 2 (Superordinate-level)
7 DATA
7.2 Wrangle
df.exp2 = df.exp2 %>%
filter(completed_all_trials == 1) %>%
mutate(condition = factor(condition,
levels = c("0", "1", "2"),
labels = c("label_only", "irrelevant", "purpose")),
across(.cols = purse : flower, .fns = as.numeric)) %>%
mutate(condition = relevel(condition, ref = "irrelevant")) %>%
pivot_longer(cols = (purse: flower),
names_to = "item",
values_to = "rating",
values_drop_na = TRUE) %>%
mutate(kind = case_when(item == "purse" ~ 0,
item == "shoe" ~ 0,
item == "plane" ~ 0,
item == "drum" ~ 0,
item == "quarter" ~ 0,
item == "apple" ~ 1,
item == "leaf_insect" ~ 1,
item == "flower" ~ 1)) %>%
mutate(kind = factor(kind,
levels = c("0", "1"),
labels = c("artifact", "biological")))
# warm-up items
df.exp2.warmup = df.exp2 %>%
filter(item %in% c("purse", "shoe"))
#test items
df.exp2 = df.exp2 %>%
filter(!item %in% c("purse", "shoe"))7.3 Demographics
## # A tibble: 7 × 2
## sex n
## <chr> <int>
## 1 0 38
## 2 1 46
## 3 Other 1
## 4 Prefer not to answer 2
## 5 not specified 1
## 6 prefer not to answer 1
## 7 prefer not to aswer 1 # language spoken
read.csv("../../data/exp2/exp2.csv") %>%
clean_names() %>%
mutate(native_english = ifelse(native_english == "-", NA, as.numeric(native_english))) %>%
summarize(sum(native_english, na.rm = TRUE))## sum(native_english, na.rm = TRUE)
## 1 788 STATS
8.1 Bayesian Regression
# sum coding
options(contrasts = c("contr.sum","contr.poly"))
df.model = df.exp2 %>%
mutate(condition = as_factor(condition),
condition= fct_relevel(condition, "purpose", "label_only", "irrelevant"))
# Check contrast values
contrasts(df.model$condition)## [,1] [,2]
## purpose 1 0
## label_only 0 1
## irrelevant -1 -1fit.brm.superordinate_level = brm(formula = rating ~ 1 + condition + (1 | participant) + (1 | item),
data = df.model,
family = "bernoulli",
seed = 1,
iter = 8000,
warmup = 1000,
cores = 8,
control = list(adapt_delta = .999999,
max_treedepth = 15),
file = "cache/fit.brm.superordinate_level")
fit.brm.superordinate_level## Family: bernoulli
## Links: mu = logit
## Formula: rating ~ 1 + condition + (1 | participant) + (1 | item)
## Data: df.model (Number of observations: 540)
## Draws: 4 chains, each with iter = 8000; warmup = 1000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.41 0.26 0.06 1.08 1.00 7926 8053
##
## ~participant (Number of levels: 90)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.84 0.17 0.51 1.19 1.00 11201 15961
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.09 0.23 -0.55 0.36 1.00 14374 15370
## condition1 0.90 0.19 0.53 1.29 1.00 19990 20055
## condition2 -0.33 0.19 -0.70 0.04 1.00 20651 20508
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).8.2 Hypotheis test of condition differneces and differnece from chance
## $emmeans
## condition emmean lower.HPD upper.HPD
## purpose 0.811 0.236 1.4289
## label_only -0.416 -0.997 0.1598
## irrelevant -0.666 -1.256 -0.0861
##
## Point estimate displayed: median
## Results are given on the logit (not the response) scale.
## HPD interval probability: 0.95
##
## $contrasts
## contrast estimate lower.HPD upper.HPD
## purpose - label_only 1.227 0.593 1.89
## purpose - irrelevant 1.474 0.840 2.15
## label_only - irrelevant 0.247 -0.385 0.89
##
## Point estimate displayed: median
## Results are given on the log odds ratio (not the response) scale.
## HPD interval probability: 0.958.3 Test of kind
fit.brm.superordinate_level.kind = brm(formula = rating ~ 1 + kind + (1 | participant) + (1 | item),
data = df.model,
family = "bernoulli",
seed = 1,
iter = 8000,
warmup = 1000,
cores = 8,
control = list(adapt_delta = .999999,
max_treedepth = 15),
file = "cache/fit.brm.superordinate_level.kind")
fit.brm.superordinate_level.kind## Family: bernoulli
## Links: mu = logit
## Formula: rating ~ 1 + kind + (1 | participant) + (1 | item)
## Data: df.model (Number of observations: 540)
## Draws: 4 chains, each with iter = 8000; warmup = 1000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.36 0.31 0.02 1.15 1.00 6099 8895
##
## ~participant (Number of levels: 90)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.06 0.17 0.73 1.42 1.00 12107 17357
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.10 0.24 -0.57 0.40 1.00 11809 10272
## kind1 -0.23 0.22 -0.66 0.20 1.00 12651 10637
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).#PLOT
8.4 Condition
df.exp2 = read.csv("../../data/exp2/exp2.csv")
df.plot = df.exp2 %>%
filter(completed_all_trials == 1) %>%
mutate(across(.cols = plane : flower, .fns = as.numeric)) %>%
select(-purse, -shoe) %>%
mutate(condition = factor(condition,
levels = c("0", "1", "2"),
labels = c("label only", "irrelevant", "purpose"))) %>%
mutate(across(.cols = plane : flower, .fns = as.numeric)) %>%
mutate(total = plane + drum + quarter + apple + leaf_insect + flower,
percent = (total / 6) * 100) %>%
mutate(condition = factor(condition, levels = c("label only", "irrelevant", "purpose"))) %>%
select(participant, condition, percent)
p3 = ggplot(data = df.plot,
mapping = aes(x = condition,
y = percent,
group = condition,
color = condition,
fill = condition)) +
stat_summary(fun = mean,
geom = "bar",
position = position_dodge(width = 0.5),
alpha = .9,
color = "black",
size = 1) +
geom_hline(yintercept = 50, linetype="dashed",
color = "black", size=.5) +
geom_point(aes(color = condition,
fill = condition),
position = position_jitterdodge(dodge.width = 0.8,
jitter.width = 0.5,
jitter.height = 0.0),
shape = 21,
size = 2,
color = "grey30",
alpha = .4) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
position = position_dodge(width = 0.5),
color = "black",
size = 1.2) +
scale_fill_manual(values = c("label only" = "#377EB8", "irrelevant" = "#E41A1C", "purpose" = "#4DAF4A"), breaks=c("label only", "irrelevant", "purpose")) +
scale_color_manual(values = c("label only" = "#377EB8", "irrelevant" = "#E41A1C", "purpose" = "#4DAF4A"), breaks=c("label only", "irrelevant", "purpose")) +
scale_y_continuous(breaks = seq(0, 100, 25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 100)) +
labs(y = "Probability of selecting \n\ taxonomic match") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size=18),
axis.text.y = element_text(size = 14),
legend.title = element_blank(),
legend.text = element_text(size = 14),
legend.position = "bottom",
axis.text.x = element_blank(),
plot.title = element_text(hjust = 0.5, face="bold", size=16, margin = margin(t = 20, b = 20))) +
ggtitle("Overall")
p3
8.5 Kind by condition
df.exp2 = read.csv("../../data/exp2/exp2.csv")
df.plot = df.exp2 %>%
filter(completed_all_trials == 1) %>%
mutate(across(.cols = plane : flower, .fns = as.numeric)) %>%
mutate(condition = factor(condition,
levels = c("0", "1", "2"),
labels = c("label only", "irrelevant", "purpose"))) %>%
select(-purse, -shoe) %>%
pivot_longer(cols = (plane: flower),
names_to = "item",
values_to = "rating",
values_drop_na = TRUE) %>%
relocate(participant, condition, item, rating, age) %>%
select(participant: age) %>%
mutate(kind = case_when( item == "plane" ~ 0,
item == "drum" ~ 0,
item == "quarter" ~ 0,
item == "apple" ~ 1,
item == "leaf_insect" ~ 1,
item == "flower" ~ 1)) %>%
mutate(kind = factor(kind,
levels = c("0", "1"),
labels = c("artifacts", "biological kinds"))) %>%
group_by(participant, condition, kind) %>%
summarise(total = sum(rating),
percent = (total / 3) * 100) %>%
mutate(condition = factor(condition, levels = c("label only", "irrelevant", "purpose"))) %>%
select(participant, condition, kind, total, percent)
p4 = ggplot(data = df.plot,
mapping = aes(x = condition,
y = percent,
group = kind,
color = kind,
fill = kind)) +
stat_summary(fun = mean,
geom = "bar",
position = position_dodge(width = 0.9),
alpha = .9,
color = "black",
size = 1) +
geom_hline(yintercept = 50, linetype="dashed",
color = "black", size=.5) +
geom_point(aes(color = kind,
fill = kind),
position = position_jitterdodge(dodge.width = 0.8,
jitter.width = 0.5,
jitter.height = 0.0),
shape = 21,
size = 2,
color = "grey30",
alpha = .4) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
position = position_dodge(width = 0.9),
color = "black",
size = 1.2) +
scale_fill_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
scale_color_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
scale_y_continuous(breaks = seq(0, 100, 25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 100)) +
labs(y = "Probability of selecting \n\ taxonomic match") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.y = element_text(size=18),
legend.title = element_blank(),
legend.text = element_text(size = 14),
legend.position = "bottom",
plot.title = element_text(hjust = 0.5, face="bold", size=16, margin = margin(t = 20, b = 20))) +
ggtitle("Artifacts vs. Biological Kinds")
p4
8.6 Combine condition and kind plots
plot_super = p3 + p4 &
plot_layout(nrow = 1) &
plot_annotation(title = "Superordinate-level categorization",
tag_levels = "A") &
theme(plot.tag = element_text(face = "bold", size = 24),
plot.title = element_text(face = "bold", hjust = 0.5, size = 24))
ggsave(plot_super, filename = "../../figures/exp2/exp2_condition_kind_combined_bar.pdf", height = 5, width = 12)
plot_super
#EXPERIMENT 3 (Norming)
#PART 1 (What is this for?)
#DATA
## Read in data
8.7 Wrangle
8.8 Demographics
## # A tibble: 5 × 2
## # Groups: gender [5]
## gender n
## <chr> <int>
## 1 N/A 1
## 2 female 28
## 3 male 14
## 4 na 1
## 5 <NA> 1## # A tibble: 3 × 2
## # Groups: native_english [3]
## native_english n
## <dbl> <int>
## 1 0 8
## 2 1 30
## 3 NA 7#PLOTS
8.8.1 Probability of top 10 words for all items
# 1 grams
df.1gram = df.exp3a %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)
df.plot = df.1gram %>%
group_by(item) %>%
count(word, sort = TRUE) %>%
group_by(item) %>%
mutate(item_total = sum(n),
probability = round(n/item_total * 100, 2),
rank = row_number(desc(probability))) %>%
filter(rank <= 10) %>%
mutate(word = reorder_within(word, probability, item)) %>%
ungroup() %>%
drop_na() %>%
separate(word, into = c("word1", "word2"), sep = "___") %>%
select(-word2) %>%
rename(word = word1)
ggplot(data = df.plot, aes(x = word, y = probability)) +
geom_hline(yintercept = 50, linetype = "dashed", color = "red") +
geom_hline(yintercept = 25, linetype = "dashed", color = "blue") +
geom_col(position = "dodge") +
facet_wrap(~ item, scales = "free_x") +
ggtitle("Probability of Words by Item") +
scale_y_continuous(breaks = seq(0, 100, 25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 100)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
8.8.2 One gram probability of selected items
# 1 grams
df.1gram_selected = df.exp3a %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)
df.plot = df.1gram_selected %>%
na.omit() %>%
group_by(item) %>%
count(word, sort = TRUE) %>%
group_by(item) %>%
mutate(item_total = sum(n),
probability = round(n/item_total * 100, 2),
rank = row_number(desc(probability))) %>%
filter(rank <= 10) %>%
mutate(word = reorder_within(word, probability, item)) %>%
ungroup() %>%
drop_na() %>%
separate(word, into = c("word1", "word2"), sep = "___") %>%
select(-word2) %>%
rename(word = word1) %>%
filter(item %in% c("chicken", "monkey", "sink", "toilet", "lion", "cow", "birdfeeder", "coffeepot", "bee", "spider", "lamp", "umbrella", "skunk", "squirrel", "plate", "clock"))
df.plot.artifacts = df.plot %>%
filter(item %in% c("sink", "toilet", "birdfeeder", "coffeepot", "lamp", "umbrella", "plate", "clock"))
df.plot.animals = df.plot %>%
filter(item %in% c("chicken", "monkey", "lion", "cow", "bee", "spider", "skunk", "squirrel"))
p5 = ggplot(data = df.plot.artifacts, aes(x = word,
y = probability)) +
geom_col(position = "dodge",
color = "black",
fill = "#984EA3") +
facet_wrap(~ item, scales = "free_x",
nrow = 2) +
scale_y_continuous(limits = c(0, 100),
expand = c(0, 0),
labels = str_c(seq(0, 100, 25), "%")) +
labs(y = "Probability of word") +
geom_hline(yintercept = 50, linetype = "dashed", color = "black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5, face="bold", size=20, margin = margin(t = 20, b = 20))) +
ggtitle("Top Artifacts")
p6 = ggplot(data = df.plot.animals, aes(x = word,
y = probability)) +
geom_col(position = "dodge",
color = "black",
fill = "#FF7F00") +
facet_wrap(~ item, scales = "free_x",
nrow = 2) +
scale_y_continuous(limits = c(0, 100),
expand = c(0, 0),
labels = str_c(seq(0, 100, 25), "%")) +
labs(y = "Probability of word") +
geom_hline(yintercept = 50, linetype = "dashed", color = "black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.title.x = element_blank(),
plot.title = element_text(hjust = 0.5, face="bold", size=20, margin = margin(t = 20, b = 20))) +
ggtitle("Top Biological Kinds")
9 PART 2 (What one X or Y, is for this?)
9.2 Wrangle
9.3 Demographics
# gender
read_csv("../../data/exp3/exp3_part2.csv") %>%
filter(participant %in% (1:40)) %>%
group_by(gender) %>%
summarise(n = n_distinct(participant)) %>%
print()## # A tibble: 2 × 2
## gender n
## <chr> <int>
## 1 female 21
## 2 male 19 # language spoken
read_csv("../../data/exp3/exp3_part2.csv") %>%
filter(participant %in% (1:40)) %>%
mutate(en_occurrence = ifelse(str_detect(language, "en"), "en", "non-en")) %>%
group_by(en_occurrence) %>%
summarise(n = n_distinct(participant)) %>%
print()## # A tibble: 2 × 2
## en_occurrence n
## <chr> <int>
## 1 en 39
## 2 non-en 1#PLOTS ## Correct Selections for Artifacts and Biological Kinds
df.plot = df.exp3b %>%
filter(item %in% c("make_honey", "feed_birds", "lay_eggs", "tell_time", "hold_coffee", "make_milk", "provide_light", "roar", "swing_from_trees", "eat_on", "wash_hands", "smell_stinky", "make_webs", "eat_nuts", "go_to_bathroom", "protect_us_from_rain")) %>%
mutate(item = as.factor(item),
item = recode(item,
"make_honey" = "bee",
"feed_birds" = "bird feeder",
"lay_eggs" = "chicken",
"tell_time" = "clock",
"hold_coffee" = "coffee pot",
"make_milk" = "cow",
"provide_light" = "lamp",
"roar" = "lion",
"swing_from_trees" = "monkey",
"eat_on" = "plate",
"wash_hands" = "sink",
"smell_stinky" = "skunk",
"make_webs" = "spider",
"eat_nuts" = "squirrel",
"go_to_bathroom" = "toilet",
"protect_us_from_rain" = "umbrella"))
df.plot.artifacts = df.plot %>%
filter(item %in% c("bird feeder", "clock", "coffee pot", "lamp", "plate", "sink", "toilet", "umbrella"))
df.plot.animals = df.plot %>%
filter(item %in% c("bee", "chicken", "cow", "lion", "monkey", "skunk", "spider", "squirrel"))
p7 = ggplot(data = df.plot.artifacts,
mapping = aes(x = item,
y = response)) +
stat_summary(fun = mean,
geom = "bar",
position = position_dodge(width = 0.5),
alpha = 0.8,
color = "black",
fill = "#984EA3",
size = 1.5) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
position = position_dodge(width = 0.5),
color = "black",
size = .7) +
# scale_fill_manual(values = my_palette) +
# scale_color_manual(values = my_palette) +
scale_y_continuous(breaks = seq(0, 1, .25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 1)) +
labs(y = "Probability of selecting \n\ object with purpose") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size=14),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, face="bold", size=20, margin = margin(t = 20, b = 20))) +
ggtitle("Top Artifacts")
p8 = ggplot(data = df.plot.animals,
mapping = aes(x = item,
y = response)) +
stat_summary(fun = mean,
geom = "bar",
position = position_dodge(width = 0.5),
alpha = 0.8,
color = "black",
fill = "#FF7F00",
size = 1.5) +
stat_summary(fun.data = mean_cl_boot,
geom = "linerange",
position = position_dodge(width = 0.5),
color = "black",
size = .7) +
# scale_fill_manual(values = my_palette) +
# scale_color_manual(values = my_palette) +
scale_y_continuous(breaks = seq(0, 1, .25),
labels = str_c(seq(0, 100, 25), "%"),
limits = c(0, 1)) +
labs(y = "Probability of selecting \n\ object with purpose") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size=14),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, face="bold", size=20, margin = margin(t = 20, b = 20))) +
ggtitle("Top Biological Kinds")
10 EXPERIMENT 3 (Transformation)
10.2 Wrangle
df.exp4 = df.exp4 %>%
select(participant_number, age_exact, condition, lion:plate) %>%
pivot_longer(cols = lion:plate,
names_to = "item",
values_to = "response") %>%
mutate(kind = ifelse(item %in% c("bee", "skunk", "lion"), "biological kinds", "artifacts"),
age_whole = as.integer(age_exact),
condition = str_replace_all(condition, "keil", "control")) %>%
rename(age = age_exact) 10.3 Demographics
# gender
read_csv("../../data/exp4/exp4.csv") %>%
group_by(gender) %>%
summarise(n = n_distinct(participant_number)) %>%
print()## # A tibble: 3 × 2
## gender n
## <chr> <int>
## 1 f 156
## 2 m 143
## 3 <NA> 1 # language spoken
read_csv("../../data/exp4/exp4.csv") %>%
mutate(en_occurrence = ifelse(str_detect(lan_spoken, "en"), "en", "non-en")) %>%
group_by(en_occurrence) %>%
summarise(n = n_distinct(participant_number)) %>%
print()## # A tibble: 2 × 2
## en_occurrence n
## <chr> <int>
## 1 en 274
## 2 <NA> 2611 STATS
11.1 Bayesian Models
11.1.1 Hypothesis 1
df.artifacts = df.exp4 %>%
filter(kind == "artifacts")
fit.brm.artifacts = brm(formula = response ~ 1 + condition*age + (1 | participant_number) + (1 | item),
data = df.artifacts,
family = "bernoulli",
seed = 1,
iter = 4000,
warmup = 1000,
control = list(adapt_delta = .9999,
max_treedepth = 15),
cores = 8,
file = "cache/fit.brm.artifacts")
fit.brm.artifacts## Family: bernoulli
## Links: mu = logit
## Formula: response ~ 1 + condition * age + (1 | participant_number) + (1 | item)
## Data: df.artifacts (Number of observations: 900)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.48 0.69 0.01 2.33 1.00 3336 4405
##
## ~participant_number (Number of levels: 300)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 4.38 0.61 3.33 5.69 1.00 2788 4831
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -16.49 4.18 -25.56 -9.15 1.00 2358
## conditionpurpose 10.76 4.50 2.56 20.50 1.00 2214
## age 1.34 0.49 0.47 2.38 1.00 2390
## conditionpurpose:age -0.55 0.55 -1.71 0.48 1.00 2246
## Tail_ESS
## Intercept 3672
## conditionpurpose 3711
## age 3395
## conditionpurpose:age 3749
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).11.1.2 Hypothesis 2
df.biological = df.exp4 %>%
filter(kind == "biological kinds")
fit.brm.biological = brm(formula = response ~ 1 + condition*age + (1 | participant_number) + (1 | item),
data = df.biological,
family = "bernoulli",
seed = 1,
iter = 4000,
warmup = 1000,
control = list(adapt_delta = .9999,
max_treedepth = 15),
cores = 8,
file = "cache/fit.brm.biological")
fit.brm.biological## Family: bernoulli
## Links: mu = logit
## Formula: response ~ 1 + condition * age + (1 | participant_number) + (1 | item)
## Data: df.biological (Number of observations: 900)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.54 0.74 0.01 2.55 1.00 3402 3704
##
## ~participant_number (Number of levels: 300)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 5.18 0.73 3.92 6.78 1.00 3238 5594
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -16.18 4.15 -25.01 -8.68 1.00 2307
## conditionpurpose 10.78 4.72 1.98 20.67 1.00 2219
## age 1.31 0.49 0.38 2.34 1.00 2316
## conditionpurpose:age -0.60 0.59 -1.80 0.53 1.00 2194
## Tail_ESS
## Intercept 3717
## conditionpurpose 3418
## age 3583
## conditionpurpose:age 3749
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).11.1.3 Hypothesis 3
df.purpose = df.exp4 %>%
filter(condition == "purpose")
fit.brm.purpose = brm(formula = response ~ 1 + kind*age + (1 | participant_number) + (1 | item),
data = df.purpose,
family = "bernoulli",
seed = 1,
iter = 4000,
warmup = 1000,
control = list(adapt_delta = .9999,
max_treedepth = 15),
cores = 8,
file = "cache/fit.brm.purpose")
fit.brm.purpose## Family: bernoulli
## Links: mu = logit
## Formula: response ~ 1 + kind * age + (1 | participant_number) + (1 | item)
## Data: df.purpose (Number of observations: 900)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.41 0.35 0.02 1.31 1.00 3587 4274
##
## ~participant_number (Number of levels: 150)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 4.96 0.64 3.85 6.37 1.00 2553 5288
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -6.85 2.66 -12.34 -1.90 1.00 1618
## kindbiologicalkinds 1.11 1.45 -1.77 4.00 1.00 6157
## age 0.90 0.35 0.26 1.61 1.00 1582
## kindbiologicalkinds:age -0.18 0.18 -0.54 0.17 1.00 6422
## Tail_ESS
## Intercept 2759
## kindbiologicalkinds 7112
## age 2839
## kindbiologicalkinds:age 7116
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).11.1.4 Hypothesis 4
df.control = df.exp4 %>%
filter(condition == "control")
fit.brm.control = brm(formula = response ~ 1 + kind*age + (1 | participant_number) + (1 | item),
data = df.control,
family = "bernoulli",
seed = 1,
iter = 4000,
warmup = 1000,
control = list(adapt_delta = .9999,
max_treedepth = 15),
cores = 8,
file = "cache/fit.brm.control")
fit.brm.control## Family: bernoulli
## Links: mu = logit
## Formula: response ~ 1 + kind * age + (1 | participant_number) + (1 | item)
## Data: df.control (Number of observations: 900)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~item (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.42 0.41 0.02 1.48 1.00 4462 6352
##
## ~participant_number (Number of levels: 150)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 4.31 0.77 3.04 6.03 1.00 2810 4600
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -15.11 3.90 -23.68 -8.14 1.00 3522
## kindbiologicalkinds 0.79 2.33 -3.80 5.46 1.00 9500
## age 1.14 0.45 0.30 2.11 1.00 3474
## kindbiologicalkinds:age 0.03 0.28 -0.54 0.58 1.00 9047
## Tail_ESS
## Intercept 5570
## kindbiologicalkinds 7919
## age 5399
## kindbiologicalkinds:age 7941
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).12 PLOTS
12.1 Bayesian Logistic Regression + Means for Purpose and Control Across Age
# df.data = expand_grid(age = seq(4, 9, 0.1),
# kind = c("artifacts", "biological kinds"))
#
# df.prediction = fit.brm.purpose %>%
# fitted(newdata = df.data,
# re_formula = NA,
# probs = c(0.1, 0.9)) %>%
# as_tibble() %>%
# bind_cols(df.data) %>%
# mutate(type = "purpose") %>%
# bind_rows(fit.brm.control %>%
# fitted(newdata = df.data,
# re_formula = NA,
# probs = c(0.1, 0.9)) %>%
# as_tibble() %>%
# bind_cols(df.data) %>%
# mutate(type = "control")) %>%
# clean_names()
#
# df.means = df.exp4 %>%
# dplyr::group_by(age_whole, kind, condition) %>%
# dplyr::summarize(
# response = Hmisc::smean.cl.boot(response)) %>%
# mutate(index = c("response", "low", "high")) %>%
# ungroup() %>%
# pivot_wider(names_from = index,
# values_from = response) %>%
# mutate(age_whole = as.numeric(as.character(age_whole)),
# age_whole = age_whole + 0.5) %>%
# filter(age_whole != 0.5)
#
#
# ggplot(mapping = aes(x = age,
# y = response,
# color = kind)) +
# geom_hline(yintercept=0.50, linetype="dashed", color = "black") +
# geom_smooth(data = df.prediction,
# mapping = aes(y = estimate,
# ymin = q10,
# ymax = q90,
# color = question,
# fill = question),
# stat = "identity",
# alpha = 0.2) +
# geom_pointrange(data = df.means,
# mapping = aes(x = age_whole,
# y = response,
# ymin = low,
# ymax = high,
# fill = question,
# shape = question),
# color = "black",
# size = 0.5,
# show.legend = FALSE,
# alpha = 0.8,
# position = position_dodge(width = 0.5)) +
# scale_fill_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
# scale_color_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds"))
# scale_shape_manual(values = c(21, 23)) +
# scale_y_continuous(breaks = seq(0, 1, 0.25),
# labels = str_c(seq(0, 1, 0.25) * 100, "%")) +
# scale_x_continuous(breaks = 5:10,
# labels = c("5", "6", "7", "8", "9", "10")) +
# labs(y = "Probability of selecting \n original thing") + theme(axis.title.y = element_text(size=12),
# legend.title = element_blank(),
# legend.position = "bottom",
# plot.title = element_text(hjust = 0.5,
# face="bold",
# size=20,
# margin = margin(t = 20, b = 20))) +
# ggtitle("Artifacts vs. Biological Kinds \n in Control and Purpose Conditions") +
# facet_wrap(~condition)12.1.1 GLM + Means
df.exp4 = df.exp4 %>%
mutate(age_whole = as.factor(age_whole),
kind = as.factor(kind))
df.points = df.exp4 %>%
group_by(participant_number, age, kind, condition) %>%
summarise(response = mean(response))
df.means = df.exp4 %>%
dplyr::group_by(age_whole, kind, condition) %>%
dplyr::summarize(
response = Hmisc::smean.cl.boot(response)) %>%
mutate(index = c("response", "low", "high")) %>%
ungroup() %>%
pivot_wider(names_from = index,
values_from = response) %>%
mutate(age_whole = as.numeric(as.character(age_whole)),
age_whole = age_whole + 0.5) %>%
filter(age_whole != 0.5)
ggplot(data = df.exp4,
mapping = aes(x = age,
y = response,
color = kind,
fill = kind)) +
geom_hline(yintercept=0.50, linetype="dashed", color = "black") +
#add points from df.points
geom_point(data = df.points,
mapping = aes(x = age,
y = response,
color = kind,
shape = kind),
alpha = 0.2,
size = 2,
position = position_jitterdodge(dodge.width = 0.8,
jitter.width = 0.5,
jitter.height = 0.0)) +
stat_smooth(method = "glm",
method.args = list(family = "binomial")) +
geom_pointrange(data = df.means,
mapping = aes(x = age_whole,
y = response,
ymin = low,
ymax = high,
fill = kind,
shape = kind),
color = "black",
size = 0.5,
show.legend = FALSE,
alpha = 0.8,
position = position_dodge(width = 0.25)) +
scale_fill_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
scale_color_manual(values = c("artifacts" = "#984EA3", "biological kinds" = "#FF7F00"), breaks=c("artifacts", "biological kinds")) +
scale_shape_manual(values = c(21, 23)) +
labs(y = "Probability of selecting \n original thing") +
scale_x_continuous(limits=c(4.9, 10),
breaks = 5:10) +
scale_y_continuous(limits = c(0, 1),
breaks = seq(0, 1, 0.25),
labels = str_c(seq(0, 1, 0.25) * 100, "%")) +
theme(axis.title.y = element_text(size=12),
legend.title = element_blank(),
legend.position = "bottom",
plot.title = element_text(hjust = 0.5,
face="bold",
size=20,
margin = margin(t = 20, b = 20))) +
ggtitle("Artifacts vs. Biological Kinds \n in Control and Purpose Conditions") +
facet_wrap(~condition)
13 SESSION INFO
## R version 4.3.3 (2024-02-29)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.2
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
## [5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
## [9] ggplot2_3.5.1 tidyverse_2.0.0 patchwork_1.2.0 janitor_2.2.0
## [13] brms_2.21.0 Rcpp_1.0.12 knitr_1.45 emmeans_1.10.1
## [17] tidytext_0.4.1
##
## loaded via a namespace (and not attached):
## [1] gridExtra_2.3 inline_0.3.19 rlang_1.1.3
## [4] magrittr_2.0.3 snakecase_0.11.1 matrixStats_1.2.0
## [7] compiler_4.3.3 mgcv_1.9-1 loo_2.7.0
## [10] systemfonts_1.0.6 vctrs_0.6.5 reshape2_1.4.4
## [13] pkgconfig_2.0.3 crayon_1.5.2 fastmap_1.1.1
## [16] backports_1.4.1 labeling_0.4.3 utf8_1.2.4
## [19] rmarkdown_2.26 tzdb_0.4.0 ragg_1.3.0
## [22] bit_4.0.5 xfun_0.43 cachem_1.0.8
## [25] jsonlite_1.8.8 tinytable_0.2.1 SnowballC_0.7.1
## [28] highr_0.10 parallel_4.3.3 cluster_2.1.6
## [31] R6_2.5.1 bslib_0.7.0 stringi_1.8.4
## [34] StanHeaders_2.32.6 rpart_4.1.23 jquerylib_0.1.4
## [37] estimability_1.5 bookdown_0.38 rstan_2.32.6
## [40] base64enc_0.1-3 bayesplot_1.11.1 splines_4.3.3
## [43] Matrix_1.6-5 nnet_7.3-19 timechange_0.3.0
## [46] tidyselect_1.2.1 rstudioapi_0.16.0 abind_1.4-5
## [49] yaml_2.3.8 codetools_0.2-19 pkgbuild_1.4.4
## [52] lattice_0.22-5 plyr_1.8.9 withr_3.0.0
## [55] bridgesampling_1.1-2 posterior_1.5.0 coda_0.19-4.1
## [58] evaluate_0.23 foreign_0.8-86 RcppParallel_5.1.7
## [61] pillar_1.9.0 janeaustenr_1.0.0 tensorA_0.36.2.1
## [64] checkmate_2.3.1 stats4_4.3.3 distributional_0.4.0
## [67] generics_0.1.3 vroom_1.6.5 hms_1.1.3
## [70] rstantools_2.4.0 munsell_0.5.1 scales_1.3.0
## [73] xtable_1.8-4 glue_1.7.0 Hmisc_5.1-2
## [76] tools_4.3.3 diffobj_0.3.5 data.table_1.15.4
## [79] tokenizers_0.3.0 mvtnorm_1.2-4 grid_4.3.3
## [82] QuickJSR_1.1.3 colorspace_2.1-0 nlme_3.1-164
## [85] htmlTable_2.4.2 Formula_1.2-5 cli_3.6.2
## [88] textshaping_0.3.7 fansi_1.0.6 Brobdingnag_1.2-9
## [91] gtable_0.3.5 sass_0.4.9 digest_0.6.35
## [94] htmlwidgets_1.6.4 farver_2.1.2 htmltools_0.5.8.1
## [97] lifecycle_1.0.4 bit64_4.0.5